We conduct experiments on two nested named entity recognition datasets: DEFT [1]} and GENIA [2]}, which present documents with different languages and different types and depths of entities (see Table REF ). For GENIA, we perform splits following [3]}: the last 10% of the sentences are used to test the model, the remaining 90% are the training set. For DEFT, we used the provided train and test splits. In both cases, we split the training data into 90% for training the model and 10% for the development (validation) set. We selected the hyperparameters by grid search on the development set and trained on the training split for GENIA, and both training and development splits for DEFT.
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